Machine Learning: Summary Greg Grudic CSCI-4830 Machine Learning 1
What is Machine Learning? The goal of machine learning is to build computer systems that can adapt and learn from their experience. Tom Dietterich Machine Learning 2
A Generic System x 1 x 2 x N System h1, h2,..., hk y 1 y 2 y M Input Variables: Hidden Variables: Output Variables: x = h = y = ( x x x ),,..., N 1 2 ( h h h ),,..., K 1 2 ( y y y ),,..., K 1 2 Machine Learning 3
Another Definition of Machine Learning Machine Learning algorithms discover the relationships between the variables of a system (input, output and hidden) from direct samples of the system These algorithms originate form many fields: Statistics, mathematics, theoretical computer science, physics, neuroscience, etc Machine Learning 4
When are ML algorithms NOT needed? When the relationships between all system variables (input, output, and hidden) is completely understood! This is NOT the case for almost any real system! Machine Learning 5
The Sub-Fields of ML Supervised Learning Reinforcement Learning Unsupervised Learning Machine Learning 6
Supervised Learning Given: Training examples {( x ( )) ( ( )) ( ( ))} 1, f x1, x2, f x2,..., xp, f xp for some unknown function (system) y = f x ( ) Find f( x) Predict y = f( x ), where x is not in the training set Machine Learning 7
Supervised Learning Algorithms Classification y Regression { 1,..., C} y R Machine Learning 8
1-R (A Decision Tree Stump) Main Assumptions Only one attribute is necessary. Finite number of splits on the attribute. Hypothesis Space Fixed size (parametric): Limited modeling potential Machine Learning 9
Naïve Bayes Main Assumptions: All attributes are equally important. All attributes are statistically independent (given the class value) Hypothesis Space Pr Fixed Pr size x (parametric): Limited modeling 1 y Pr x2 y Pr xd y potential y x = Pr [ x] Machine Learning 10
Linear Regression Main Assumptions: Linear weighted sum of attribute values. Data is linearly separable. Attributes and target values are real valued. Hypothesis Space Fixed size (parametric) : Limited modeling potential d = i i + i= 1 y a x b Machine Learning 11
Linear Regression (Continued) Linearly Separable Not Linearly Separable Machine Learning 12
Decision Trees Main Assumption: Data effectively modeled via decision splits on attributes. Hypothesis Space Variable size (nonparametric): Can model any function Machine Learning 13
Neural Networks Main Assumption: Many simple functional units, combined in parallel, produce effective models. Hypothesis Space Variable size (nonparametric): Can model any function Machine Learning 14
Neural Networks (Continued) Machine Learning 15
Neural Networks (Continued) Learn by modifying weights in Sigmoid Unit Machine Learning 16
K Nearest Neighbor Main Assumption: An effective distance metric exists. Hypothesis Space Variable size (nonparametric): Can model any function Classify according to Nearest Neighbor Separates the input space Machine Learning 17
Bagging Main Assumption: Combining many unstable predictors to produce a ensemble (stable) predictor. Unstable Predictor: small changes in training data produce large changes in the model. e.g. Neural Nets, trees Stable: SVM, nearest Neighbor. Hypothesis Space Variable size (nonparametric): Can model any function Machine Learning 18
Bagging (continued) Each predictor in ensemble is created by taking a bootstrap sample of the data. Bootstrap sample of N instances is obtained by drawing N example at random, with replacement. On average each bootstrap sample has 63% of instances Encourages predictors to have uncorrelated errors. Machine Learning 19
Boosting Main Assumption: Combining many weak predictors (e.g. tree stumps or 1-R predictors) to produce an ensemble predictor. Hypothesis Space Variable size (nonparametric): Can model any function Machine Learning 20
Boosting (Continued) Each predictor is created by using a biased sample of the training data Instances (training examples) with high error are weighted higher than those with lower error Difficult instances get more attention Machine Learning 21
Machine Learning 22
Support Vector Machines Main Assumption: Build a model using minimal number of training instances (Support Vectors). Hypothesis Space Variable size (nonparametric): Can model any function Based on PAC (probably almost correct) learning theory: Minimize the probability that model error is greater than (small number) ε Machine Learning 23
Linear Support Vector Machines Support Vectors Machine Learning 24
Nonlinear Support Vector Machines Project into Kernel Space (Kernels constitute a distance metric in inputs space) Machine Learning 25
Competing Philosophies in Supervised Learning Goal is always to minimize the probability of model errors on future data! A single Model: Motivation - build a single good model. Models that don t adhere to Occam s razor: Minimax Probability Machine (MPM) Trees Neural Networks Nearest Neighbor Radial Basis Functions Occam s razor models: The best model is the simplest one! Support Vector Machines Bayesian Methods Other kernel based methods: Kernel Matching Pursuit Machine Learning 26
Competing Philosophies in Supervised Learning An Ensemble of Models: Motivation a good single model is difficult to compute (impossible?), so build many and combine them. Combining many uncorrelated models produces better predictors... Models that don t use randomness or use directed randomness: Boosting Specific cost function Gradient Boosting Derive a boosting algorithm for any cost function Models that incorporate randomness: Bagging Bootstrap Sample: Uniform random sampling (with replacement) Stochastic Gradient Boosting Bootstrap Sample: Uniform random sampling (with replacement) Random Forests Uniform random sampling (with replacement) Randomize inputs for splitting at tree nodes Machine Learning 27
Evaluating Models Infinite data is best, but N (N=10) Fold cross validation Create N folds or subsets from the training data (approximately equally distributed with approximately the same number of instances). Build N models, each with a different set of N-1 folds, and evaluate each model on the remaining fold Error estimate is average error over all N models Machine Learning 28
Boostrap Estimate Machine Learning 29
Reinforcement Learning (RL) Autonomous agent learns to act optimally without human intervention Agent learns by stochastically interacting with its environment, getting infrequent rewards Goal: maximize infrequent reward Machine Learning 30
Q Learning Machine Learning 31
Agent s Learning Task Machine Learning 32
Unsupervised Learning Studies how input patterns can be represented to reflect the statistical structure of the overall collection of input patterns No outputs are used (unlike supervised learning and reinforcement learning) unsupervised learner brings to bear prior biases as to what aspects of the structure of the input should be captured in the output. Machine Learning 33
Expectation Maximization (EM) Algorithm Clustering of data K-Means Estimating unobserved or hidden variables Machine Learning 34